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This paper presents some performance results for the Moving Time Window (MTW) parallel simulation control protocol: a scheduling paradigm for parallel discrete-event simulation. MTW supports both optimistic and conservative event execution models via a time window. The time window constrains simulation object asynchrony by temporally bounding the difference(More)
Data mining can be used effectively to detect health care fraud and abuse. First, data mining has been successful at applying visualization to very large data sets and recognizing new and unusual patterns of activity. Second, data mining has allowed us to better direct and utilize limited health care fraud detection and investigative resources by(More)
Much of the research that goes into Big Data, and specifically on Collaborative Big Data, is focused upon questions, such as: how to get more of it? (e.g., · participatory mechanisms, social media, geo-coded data from personal electronic devices) and · how to handle it? (e.g., how to ingest, sort, store, and link up disparate data sets). A(More)
This paper presents the proposed architecture for ISLE, an intelligent Scalable Logistics Environment. ISLE incorporates three different solution techniques: simulation, AI and OR in a massively parallel environment. ISLE is based on data parallelism. ISLE supports an incremental technique for scheduling. It can represent illegal schedules, e.g., schedules(More)
This study develops the framework for an abusive-billing detection system that can potentially be used for examining claims of Medicare home health agencies, skilled nursing facilities, hospital outpatient facilities and hospice facilities. This system, which utilizes Medicare claims and other data, is comprised of two components: (1) a single-linkage(More)